DataRobot
Your AI agent interfaces directly with DataRobot's API to check project status, list deployments, monitor model accuracy, and manage user access. Teams that rely on predictive models can now get answers about deployment health and model performance through a simple conversation instead of navigating complex MLOps dashboards.
Your AI agent connects to DataRobot's full API surface, from project creation to deployment monitoring, making machine learning operations accessible to everyone on the team.
DataRobot
See how data science and business teams use AI agents to interact with DataRobot's ML platform without needing deep technical knowledge of the underlying API.
Before a quarterly business review, a product manager asks 'How is the churn prediction model performing this month?' Your AI Agent queries DataRobot's deployment accuracy endpoint, pulls accuracy metrics and data drift indicators for the specified deployment, and summarizes whether performance meets the defined SLO thresholds. The PM presents data-backed model health to stakeholders without filing a single request to the data science team.
A data scientist kicks off a new DataRobot autopilot run and goes to lunch. They message the agent later asking for the status. The agent checks the project status API, reports that 47 out of 60 models have been trained, and notes two models currently in the queue. The scientist knows exactly when to come back and review the leaderboard, without context-switching from their other work.
An ML governance lead needs to audit all active deployments. Your AI Agent retrieves the full deployment list from DataRobot, filtered by service health and last prediction activity. The agent surfaces deployments that have not received predictions in over 30 days and flags those with failing accuracy health. The governance team gets an instant audit snapshot instead of manually reviewing each deployment in the UI.
DataRobot
FAQs
The agent can list and inspect projects, deployments, model packages, datasets, credentials, user groups, batch jobs, feature lists, and prediction environments. It also accesses deployment-level metrics like accuracy over time, service health, and capability configurations. Access scope matches the permissions of your DataRobot API key.
Yes. The integration supports creating projects from dataset URLs or existing catalog datasets, starting autopilot with configurable modes like quick, comprehensive, or auto, and monitoring job progress. You can restrict write operations through your API key permissions if you prefer read-only access.
The agent calls DataRobot's accuracy over time and deployment accuracy endpoints, which return metric values bucketed by time period. It can compare current performance against baseline models and surface drift indicators. The response includes specific metric names like AUC, RMSE, or LogLoss depending on your deployment configuration.
No. All data is fetched live from DataRobot's API during conversations. Project details, deployment metrics, and dataset catalogs are queried on demand and used only to generate the agent's response. Tars does not maintain a copy of your ML platform data.
Yes. The agent can create user groups, add or remove members, list group memberships, and manage custom access roles with specific permission sets. This is useful for onboarding new team members or adjusting project access through conversational commands rather than the admin UI.
The integration uses a DataRobot API key paired with your deployment's base URL. You generate the API key from your DataRobot account settings. The key determines which resources the agent can access, and you can create keys scoped to specific organizations or permission levels.
DataRobot's native tools require users to work inside the platform. Tars AI Agents bring DataRobot data to where your team already works, whether that is Slack, WhatsApp, or a web chat. Non-technical stakeholders can ask about model health without learning the DataRobot interface, and the same agent can pull data from other tools like Jira or Salesforce in the same conversation.
Yes. The agent can create batch prediction job definitions with intake and output settings, configure cron-like schedules, and list running batch jobs with their status. This lets data engineers manage scoring pipelines through conversation instead of navigating the batch predictions UI.
Don't limit your AI Agent to basic conversations. Watch how to configure and add powerful tools making your agent smarter and more functional.
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